P1393A SYSTEM FOR FORECASTING OF DRUG DOSAGE FOR BONE MINERAL DISORDERS CORRECTION IN PATIENTS ON CHRONIC DIALYSIS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Dmitriy Zinovev ◽  
Vladimir Novitskiy ◽  
Andrey Malkoch
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Drug Dosage ◽  
Chronic Dialysis ◽  
Target Range ◽  
Forecasting Models ◽  
Number Of Patients ◽  
Forecasting System ◽  
Artificial Neural ◽  
Range Of Values

Abstract Background and Aims Bone and mineral disorders (BMD) is a common complication of CKD in patients on chronic dialysis. Timely and adequate correction of BMD is the most important aspect of patient's treatment. This work presents a system for forecasting of phosphate-binding agents (PBA) dosage and vitamin D receptor activators (VDRA) dosage. The system consists of sequentially triggering artificial neural network forecasting models (separate model for each drug type). Method As an input dataset, system uses patient’s results of laboratory studies (blood calcium, phosphate and PTH) for the period of 6 months, information on previous drug therapy and data on adequacy of patient’s dialysis therapy. The output of the system are dosages of PBA an VDRA that have to be administered in order to bring the patient’s parameters as close as possible to target range of values (2.1-2.5 mmol/l for calcium, 0.87-1.5 mmol/l for phosphate and 150-300 pg/ml for PTH). The system consists of two sequentially triggering forecasting models (for PBA and for VDRA), where each model is an artificial neural network, that has been trained on a data, collected in more than 20 “Nefrosovet” hemodialysis clinics for the period of 3 years. The effect of system usage was examined for the group of 356 hemodialysis patients with median follow-up time of 3 month. The primary end-points were a number of patients in target range of values With respect to calcium (2.1-2.5 mmol/l), phosphate (0.87-1.5 mmol/l) and PTH (150-300 pg/ml). Results During the study we determined that as a result of using the dose forecasting system, number of patients in target range of values significantly increased with respect to calcium (from 178 to 209, p=.0196), phosphate (from 99 to 152, p=.0000), and PTH (from 83 to 109, p=.0281). Conclusion Employment of automated drug dosage forecasting system based on artificial neural network models, has a positive effect on BMD correction quality, which, in turn, reduces the risk of possible complications.

P1380A SYSTEM FOR FORECASTING OF DRUG DOSAGE FOR ANEMIA CORRECTION IN PATIENTS ON CHRONIC DIALYSIS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Dmitriy Zinovev ◽  
Vladimir Novitskiy ◽  
Andrey Malkoch
Keyword(s):  
Drug Therapy ◽  
Drug Dosage ◽  
Hemodialysis Patients ◽  
Chronic Dialysis ◽  
Target Range ◽  
Iron Supplements ◽  
System Usage ◽  
Number Of Patients ◽  
Forecasting System ◽  
Logical Rules

Abstract Background and Aims Anemia is a most common complication of CKD in patients on chronic dialysis. Adequacy of anemia correction directly affects both patient’s life quality and patient’s long-term survival. The most important aspect of anemia correction is drug therapy. In this work, we present a system for forecasting of iron supplements and ESA (Epoetin alfa) dosage, that is based on logical rules and artificial intelligence (AI) models. Method As an input dataset, system uses patient’s anthropomorphic parameters, results of laboratory studies, and information on previous drug therapy. The output of the system are dosages of ESA an iron supplements that have to be administered in order to bring the patient’s hemoglobin as close as possible to target range of values (100-120 g/l). The system consists of two sequentially triggering forecasting models (for ESA and for iron supplements), where each model is a combination of logical rules and artificial neural network, that has been trained on a data, collected in more than 20 “Nefrosovet” hemodialysis clinics for the period of 3 years. The effect of system usage was examined for the group of 356 hemodialysis patients with median follow-up time of 4 month. The primary end-point was a number of patients in target range of hemoglobin values (100-120 g/l). Results During the study we determined that as a result of using the dose forecasting system, number of patients in target range of hemoglobin values significantly increased from 239 patients at the beginning of system employment to 266 patients at the end of the study (p=.0318). Furthermore, we observed that there was a concomitant effect of system usage – significant reduction of average monthly ESA dosage from 14300 IU at the beginning of system employment to 13900 IU at the end of the study (p=.0331). Conclusion Employment of automated drug dosage forecasting system based on logical rules and AI models, allows to improve the efficiency of anemia correction in hemodialysis patients and reduce the dosage of administered ESA, which, in turn, reduces the risk of possible complications and treatment cost.

scholarly journals Operational Probabilistic Forecasting of Coastal Freak Waves by Using an Artificial Neural Network

2020 ◽  
Vol 8 (3) ◽  
pp. 165
Author(s):  
Dong-Jiing Doong ◽  
Shien-Tsung Chen ◽  
Ying-Chih Chen ◽  
Cheng-Han Tsai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Freak Waves ◽  
Forecasting Models ◽  
Artificial Neural

Coastal freak waves (CFWs) are unpredictable large waves that occur suddenly in coastal areas and have been reported to cause casualties worldwide. CFW forecasting is difficult because the complex mechanisms that cause CFWs are not well understood. This study proposes a probabilistic CFW forecasting model that is an advance on the basis of a previously proposed deterministic CFW forecasting model. This study also develops a probabilistic forecasting scheme to make an artificial neural network model achieve the probabilistic CFW forecasting. Eight wave and meteorological variables that are physically related to CFW occurrence were used as the inputs for the artificial neural network model. Two forecasting models were developed for these inputs. Model I adopted buoy observations, whereas Model II used wave model simulation data. CFW accidents in the coastal areas of northeast Taiwan were used to calibrate and validate the model. The probabilistic CFW forecasting model can perform predictions every 6 h with lead times of 12 and 24 h. The validation results demonstrated that Model I outperformed Model II regarding accuracy and recall. In 2018, the developed CFW forecasting models were investigated in operational mode in the Operational Forecast System of the Taiwan Central Weather Bureau. Comparing the probabilistic forecasting results with swell information and actual CFW occurrences demonstrated the effectiveness of the proposed probabilistic CFW forecasting model.

scholarly journals FLOOD FORECASTING MODEL USING THE COMBINATION APPROACH

2020 ◽  
Vol 1 (2) ◽  
pp. 59-64
Author(s):  
Hu Weighuo ◽  
Hu He
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Flood Forecasting ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Highly Efficient ◽  
Artificial Neural ◽  
Combination Approach

This paper reviews the qualities of a good flood forecasting model such as timeliness, accuracy, and reliability. The article reviews the current forecasting models which are based on fuzzy logic, artificial neural network, as well as combination. The combination approach is gaining popularity and is found to be more flexible, accurate, reliable, and highly efficient in terms of development and output.

scholarly journals Remote-Sensing-Based Streamflow Forecasting Using Artificial Neural Network and Support Vector Machine Models

2021 ◽  
Vol 13 (20) ◽  
pp. 4147
Author(s):  
Mohammed M. Alquraish ◽  
Mosaad Khadr
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Superior Performance ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Forecasting Models ◽  
Monthly Streamflow ◽  
Artificial Neural ◽  
Monthly Streamflow Forecasting

In this study, we aimed to investigate the hydrological performance of three gridded precipitation products—CHIRPS, RFE, and TRMM3B42V7—in monthly streamflow forecasting. After statistical evaluation, two monthly streamflow forecasting models—support vector machine (SVM) and artificial neural network (ANN)—were developed using the monthly temporal resolution data derived from these products. The hydrological performance of the developed forecasting models was then evaluated using several statistical indices, including NSE, MAE, RMSE, and R2. The performance measures confirmed that the CHIRPS product has superior performance compared to RFE 2.0 and TRMM data, and it could provide reliable rainfall estimates for use as input in forecasting models. Likewise, the results of the forecasting models confirmed that the ANN and SVM both achieved acceptable levels of accuracy for forecasting streamflow; however, the ANN model was superior (R2 = 0.898–0.735) to the SVM (R2 = 0.742–0.635) in both the training and testing periods.

Procedure of training artificial neural network of elastic-damping characteristic control system of shock absorber landing gear struts of ground-based aircraft

2019 ◽  
Author(s):  
V.A. Brusov ◽  
Yu.Yu. Merzlikin ◽  
A.S. Menshikov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shock Absorber ◽  
Adaptive System ◽  
Landing Gear ◽  
Damping Characteristics ◽  
Wide Range ◽  
Artificial Neural ◽  
Run Up ◽  
Range Of Values

During their calendar life, passenger and transport aircraft run more than 200 thousand kilometers on the runways, which cause a significant part of the damage, both in the landing gears and in other units of the airframe. To reduce aircraft overloads at the stages of takeoff and landing (run-up and run on the runway) and taxiing, shock-absorbing struts with variable elastic-damping characteristics are used. Due to the fact that the parameters of the runway irregularities are in a wide range of values, it is necessary to use an adaptive system for controlling the stiffness coefficients and damping of the shock absorber strut, designed using an artificial neural network. The paper considered a network containing three layers. Using such a model, it is possible to implement an adaptive control circuit adjusting the elastic-damping parameters of the aircraft shock absorber struts to specific runway conditions (length and height of the irregularity, specific hardness of the runway). The velocity gradient method was used to train the artificial neural network. Half the square of the mismatch signal was used as the target criterion to be minimized. The calculated studies of the run up and run of the Il-114 aircraft on a dirt runway showed the possibility of reducing vertical overloads by up to 15% when equipped with a system controlling elastic-damping characteristics with a neural network. The comparison was carried out with an aircraft equipped with a “classical” (non-adaptive) system for controlling landing gear parameters.

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